A power amplifier (PA) is a device used in many communication systems which takes as input a low power desired signal and produces as output a higher power version of the desired signal. Although PA's can be used as linear devices, they are often pushed to operate in their non-linear region because such operation can cause the PA to produce more output power and can also cause the PA to operate more efficiently. However, operating the PA in the nonlinear region comes at a cost in that the output of the PA now contains both a scaled up version of the desired signal, and also a significant amount of distortion.
Techniques exist whereby the signal coming into the PA is modified so that although the input to the PA differs radically from the desired signal, the output coming out of the PA is, however, a scaled up version of the desired signal. Some examples of techniques which can produce a PA input signal which will produce an arbitrary signal on the PA output are digital predistortion (DPD) or analog predistortion based on a certain assumed PA model.
These techniques, however, are limited in performance because they assume that the PA's characteristics can be modeled sufficiently by a specific model. These assumed models are only accurate up to a certain limit and hence the ultimate performance that can be obtained through the use of these techniques is also limited. It would be of benefit if it would be possible to create an arbitrary output on any PA with an arbitrary amount of output signal quality performance.
Digital predistortion (DPD), as shown in FIG. 1 (prior art), refers to a method in a communications system where the signal to be transmitted [5] is passed through a nonlinear predistortion function [2] before being sent to the nonlinear power amplifier PA [1]. The general idea is that the nonlinear predistortion function [2] is chosen such that the cascaded combination of the nonlinear predistortion function [2] and the nonlinear PA [1] will produce a system that is linear overall. Thus, the actual PA output [6], will simply be a scaled up version of the input to the predistortion function [2].
The predistortion function [2] is often inferred using the indirect learning architecture as depicted in FIG. 1 (prior art). A coupler [4], is used to extract a small amount of the power generated by the PA [1]. This signal is sent through an adaptable inverse PA model [3], which produces an output that is adapted to match, as closely as possible, the input to the PA [1]. This adaptation is often implemented as an LMS or an RLS algorithm, although it is possible to use other algorithms.
Once the difference between the output of the of the inverse PA model [3] and the PA input is substantially small enough (as measured by the power of the difference signal), the inverse PA model [3] is simply copied and used directly as the predistortion function [2]. No model inversion is necessary because the indirect learning architecture described in FIG. 1 (prior art) directly calculates the inverse model without first calculating the forward model.
Usually, this process needs to be repeated several times or over several iterations before the output of the PA [1] is considered to be satisfactory, as measured by common signal transmission quality measurements such as error vector magnitude (EVM) or adjacent channel leakage ration (ACLR).
A problem with the above structure is the difficulty in choosing the model to be used for the inverse PA model [3]. Common structures that are used are the Memory Polynomial (MP) or the Generalized Memory Polynomial (GMP), although other models can also be used. Before actually trying a certain model, it is not clear which model will yield the best performance, and it is also not clear which specific parameters for a particular model will yield the best performance. For example, with the Memory Polynomial model, one can specify both the maximum delay of the model and also the maximal nonlinear order of the model. It is not clear, in advance, whether the GMP will perform better than the MP and which settings for maximum delay and nonlinear order in the MP will produce the best results. Furthermore, because the PA [1] and the inverse PA model [3] are nonlinear entities, there are no known search algorithms available which will find the optimal models or model settings.
Typically, the inverse PA model is found by running the system in a lab environment with a real PA. A certain model and certain model settings are chosen and, after the system converges, one observes the final system performance using measurements such as EVM and ACLR. Then, the model and/or the settings are changed and the final system performance is measured again. This procedure may proceed for hundreds of measurements where each measurement may require several minutes of lab time. Finally, the model and settings which resulted in the best performance are chosen and used in the final system.
It would be beneficial if there was a method of improving the speed at which the optimal inverse PA model could be found.
An alternate form of DPD based on PA model inversion is shown in FIG. 2 (prior art). As in the indirect learning architecture, the signal to be transmitted [5] is passed through a nonlinear predistortion function [2] before being sent to the nonlinear power amplifier PA [1]. The general idea is that the nonlinear predistortion function [2] is chosen such that the cascaded combination of the nonlinear predistortion function [2] and the nonlinear PA [1] will produce a system that is linear overall. Thus, the actual PA output [6], will simply be a scaled up version of the input to the predistortion function [2].
With model inversion DPD, the forward PA model [7] is known and is an accurate predictor of the PA's output, given a certain input. This forward PA model may arise from measurements of an actual PA, or it may arise from a mathematical model derived by analyzing the circuit diagram of the PA. The key is that this model must be inverted and then used as the predistortion function.
One of the major problems with this architecture is that although the PA forward model may in fact be very simple, the inversion of even a very simple nonlinear model is often very difficult and requires a model that is several orders more complex than the PA model itself.
It would be beneficial if a method existed to linearize the PA without needing to invert the PA model.